TY - GEN
T1 - IVA-Based Spatio-Temporal Dynamic Connectivity Analysis in Large-Scale FMRI Data
AU - Bhinge, Suchita
AU - Calhoun, Vince D.
AU - Adali, Tulay
N1 - Funding Information:
This work was supported in parts by grants NIH-NIBIB R01 EB 020407 and NSF-CCF 1618551.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - Recently, much attention has been devoted to examining time-varying changes in functional connectivity to understand the network structure in the human brain. Most studies, however, analyze the time-varying functional connectivity but ignore the time-varying spatial information. In this paper, we propose a method based on independent vector analysis (IVA) to study dynamic functional network connectivity (dFNC) as well as dynamic spatial functional network connectivity (dsFNC) in fMRI data. Though IVA allows one to effectively capture both, its performance degrades with the increase in the number of datasets. Hence, we propose an effective scheme to bypass this limitation followed by graph theoretical analysis to study both inter-network dynamics and intra-network stationarity. We observe higher dFNC fluctuations for patients with schizophrenia in the default-mode (DM)-salience network and cerebellum with associated connections. dsFNC analysis indicates higher inter-network fluctuation in patients while DM, anterior DM and frontal networks demonstrate significant intra-network fluctuation in controls.
AB - Recently, much attention has been devoted to examining time-varying changes in functional connectivity to understand the network structure in the human brain. Most studies, however, analyze the time-varying functional connectivity but ignore the time-varying spatial information. In this paper, we propose a method based on independent vector analysis (IVA) to study dynamic functional network connectivity (dFNC) as well as dynamic spatial functional network connectivity (dsFNC) in fMRI data. Though IVA allows one to effectively capture both, its performance degrades with the increase in the number of datasets. Hence, we propose an effective scheme to bypass this limitation followed by graph theoretical analysis to study both inter-network dynamics and intra-network stationarity. We observe higher dFNC fluctuations for patients with schizophrenia in the default-mode (DM)-salience network and cerebellum with associated connections. dsFNC analysis indicates higher inter-network fluctuation in patients while DM, anterior DM and frontal networks demonstrate significant intra-network fluctuation in controls.
KW - Dynamic functional connectivity
KW - Dynamic spatial connectivity
KW - Independent vector analysis
KW - Network stationarity
KW - Temporal graphs
UR - http://www.scopus.com/inward/record.url?scp=85054245136&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054245136&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8461947
DO - 10.1109/ICASSP.2018.8461947
M3 - Conference contribution
AN - SCOPUS:85054245136
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 965
EP - 969
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -